/Engine/Plugins/Experimental/LearningAgents/Source/LearningAgentsTraining/Public/LearningAgentsTrainer.h
The configurable settings for the training process.
[USTRUCT](Programming/UnrealArchitecture/Reference/Structs)([BlueprintType](Programming/UnrealArchitecture/Reference/Structs/Specifiers/BlueprintType), Category="LearningAgents") struct FLearningAgentsTrainerTrainingSettings
float
ActionRegularizationWeight
Weight used to regularize actions.
bool
bAdvantageNormalization
When true, advantages are normalized.
int32
BatchSize
Batch size to use for training.
bool
bClipAdvantages
When true, very large or small advantages will be clipped.
bool
bUseTensorboard
If true, TensorBoard logs will be emitted to the intermediate directory.
ELearningAgents...
Device
The device to train on.
float
DiscountFactor
The discount factor to use during training.
float
EntropyWeight
Weighting used for the entropy bonus.
float
EpsilonClip
Clipping ratio to apply to policy updates.
float
GaeLambda
This is used in the Generalized Advantage Estimation as what is essentially an exponential smoothing/decay.
float
InitialActionScale
The initial scaling for the weights of the output layer of the neural network.
float
LearningRateCritic
Learning rate of the critic network.
float
LearningRateDecay
Ratio by which to decay the learning rate every 1000 iterations.
float
LearningRatePolicy
Learning rate of the policy network. Typical values are between 0.001 and 0.0001.
int32
NumberOfIterations
The number of iterations to run before ending training.
int32
NumberOfStepsToTrimAtEndOfEpisode
The number of steps to trim from the end of the episode.
int32
NumberOfStepsToTrimAtStartOfEpisode
The number of steps to trim from the start of the episode, e.g. can be useful if some things are still getting setup at the start of the episode and you don't want them used for training.
int32
RandomSeed
The seed used for any random sampling the trainer will perform, e.g. for weight initialization.
float
WeightDecay
Amount of weight decay to apply to the network.